Advanced driver-assistance system on Raspberry Pi using CNN, Python and OpenCV

Eye state localisation and detection for use in Advanced Driver Assistance System.

Get any car to drive itself with a highly flexible reimplementation of openpilot.

AutoSeatAdjust is an embedded AI project that uses real-time camera-based eye detection to automatically adjust the driver’s seat. It trains an XGBoost regression model on a dataset of eye coordinates mapped to seat positions, enabling the system to predict seat distance and height adjustments for optimal comfort and safety.

Creating a driver drowsiness detection project using the Haar -Cascade algorithm can be a valuable contribution to the open-source community

Real-time driver drowsiness and distraction detection using MediaPipe face mesh, YOLOv8 for phone detection, and a PyTorch LSTM; Streamlit UI for live webcam monitoring.

Real-time lane detection system using OpenCV and Python for autonomous vehicle applications

AI-Powered Smart Road Visibility & Hazard Detection System for smoggy conditions.

STM32-based Smart Parking Assistance System using HC-SR04 ultrasonic sensors, Embedded C, STM32 HAL, LCD, buzzer, LEDs, and modular firmware architecture for real-time obstacle detection and driver guidance.

ADAS inspired driver assistance system using Raspberry Pi, OpenCV, lane departure detection, ultrasonic obstacle sensing, LCD alerts and haptic feedback

Real-time, 100% on-device ADAS for Android: forward-collision, drivable-area segmentation, pedestrian, lane and traffic-sign warnings that self-tune from budget phones to flagships.

Predictive vehicle stability warning for race cars using IMU, steering, speed telemetry, vehicle dynamics, and ML.

🚗 Real-time lane detection using Python & OpenCV with image and video processing pipeline visualization.

Water puddle detection on roads using YOLO-based object detection for driver-assistance and autonomous driving applications.

Real-time AI Driver Assistant using Python, OpenCV, YOLOv8, object tracking, distance estimation, voice alerts, and futuristic dashboard UI.

🏎️Arduino safety system for eKarts/go-karts with obstacle & overheat alerts, FSM ignition, GPS tracking, SMS notifications, and visual/audio warnings.

Computer vision system for driver support and hazard warning in airport airside environments. Real-time video analysis, surface and ground-marking segmentation using YOLO, U-Net, DeepLabV3 and SegFormer, with PC and Raspberry Pi/Hailo deployment.

Confidence-aware multilingual voice assistant for driver support using speech recognition, NLP, confidence-based response validation, and text-to-speech for safe hands-free interaction.